ABSTRACT Sarcoidosis is a systemic inflammatory disease of unknown etiology characterized by non-caseating granulomas in affected organs, primarily in the lungs. Approximately 30% of patients with sarcoidosis progress to debilitating disease; however, the drivers of susceptibility or resilience to disease remain poorly understood. An inflammatory response to an undefined antigen is postulated as the etiology of granuloma formation, and the pathogenesis has been suggested to involve gene-pathogen interaction, yet analysis of single genes or microbes has not proven applicable to diagnosis of all forms of sarcoidosis. Indeed, rather than a single organism, the disease may represent an interaction between the community of organisms that comprise the lung microbiome (community of organisms that live in and on us) and the host immune response. We propose that understanding the microbiome/host interaction will suggest strategies for precision medicine approaches to sarcoidosis. This proposal addresses this significant gap by investigating interactions between the lung microbiome, host immune and clinical responses in sarcoidosis using multiomics approaches ? a critically innovative strategy. Our preliminary data support our novel hypotheses. First, we identified distinct lung microbiomes that differentiated patients with sarcoidosis versus controls. Second, our results identified biomarkers of disease severity that were associated with decreased lung function. Third, a recurrent analytic theme that emerged, regardless of the type of -omic analysis, was that sarcoidosis is characterized by pathways related to apoptosis and autophagy, which is consistent with our observation of decreased abundance of peripheral lymphocytes and functional immune anergy. These data led us to our Overall Hypothesis: Lung microbiome and host immune interactions characterized by apoptosis and autophagy pathways influence sarcoidosis clinical course. This hypothesis will be tested by an observational prospective and validation study of sarcoidosis patients at 5 time points to facilitate time series analyses. Aims 1 and 2 focus on lung microbiome or host immune responses, respectively, in relation to clinical course of sarcoidosis. Using these data in Aim 3, predictive models will be constructed based on integrated data of metagenomic and host-immune interactions. The novelty and significance of our multiomics strategy is to construct models for precision medicine therapies to harness bioinformatic strategies into focused, patient-specific approaches. The long-term significance of this study is to define pathways for sarcoidosis progression or resolution, and to develop database of these findings to further develop more precise, testable, models.